Okay, so today is the second part of paradigms or language ideas that don't really fit into

a bigger category.

So onward we go.

So tons, I've distributed them, you can cut them out yourself and then have them.

So what is, when programming, something for the real world.

Nothing in the real world is exact.

Nothing is discrete.

You do not know how large a tree is.

You know it's plus, minus six meters plus, minus a few centimeters.

You never know anything exactly in the real world.

You don't know how fast the car is going exactly.

You know it's about 50 kilometers per hour plus, minus a little bit.

So we need to be able to deal with uncertainties when programming something for the real world.

For embedded systems, for example.

For this we need programming languages that support uncertainty in a first level basis.

For example we need statistical variables and potentially for something for the future,

quantum variables.

There is some research going on now for programming languages for quantum computers.

You don't have quantum computers yet so let's take this with a grain of salt.

What is real is physiologic.

Physiologic is a fairly old concept for programming with uncertainties.

So you're not sure in what category something belongs to.

You don't know how large something is in a quantity.

So uncertain categories and uncertain sizes of things.

Light is almost certainly quantum protocol based.

Can we model that in a probabilistic or physiologic?

Or is it a bird, is it a plane, or is it Superman?

You don't know what kind of category something is.

Therefore it's a fuzzy problem, fuzzy categorization.

So there's fuzzy logic rules for inference with uncertain quantities.

For example if a pizza is good and if it has good bread and it has good sauce then you

know that a pizza is good.

But this is something subjective.

You never know if something is really good and how good is it?

Is it 100% good?

Is it 80% good?

You don't know.

So something, some statement like this is inexact as well.

So you need to be able to reason with inexact quantities.

For a pizza parlor you have uncertain quantities and you need to make actions with certainty

or even then probabilistic actions.

So the first idea here is to have intervals.

Something is good if it's an interval between 90% sure and 100% sure then something is good.

If something is in the range of 50% to 80% then we'll say it's probably okay and if it's

less than 50% we say it's bad.

So this is the idea of interval based logic.

So we have a set of values and something is a memory of a set if it falls into a certain

probabilistic range.

So set ranges are mostly from 0 to 1.

Statistics.